Bounding convergence rates for Markov chains: An example of the use of computer algebra

Research output: Contribution to journalArticle

Abstract

Kolassa and Tanner (J. Am. Stat. Assoc. (1994) 89, 697-702) present the Gibbs-Skovgaard algorithm for approximate conditional inference. Kolassa (Ann Statist. (1999), 27, 129-142) gives conditions under which their Markov chain is known to converge. This paper calculates explicity bounds on convergence rates in terms calculable directly from chain transition operators. These results are useful in cases like those considered by Kolassa (1999).

Original languageEnglish (US)
Pages (from-to)83-87
Number of pages5
JournalStatistics and Computing
Volume11
Issue number1
DOIs
StatePublished - Dec 1 2001

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Computational Theory and Mathematics

Keywords

  • Computer algebra
  • Markov chain
  • Monte Carlo

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